Ratio of geometric means to analyze continuous outcomes in meta‐analysis: comparison to mean differences and ratio of arithmetic means using empiric data and simulation
Bibliographic record
Abstract
Meta-analyses pooling continuous outcomes can use mean differences (MD), standardized MD (MD in pooled standard deviation units, SMD), or ratio of arithmetic means (RoM). Recently, ratio of geometric means using ad hoc (RoGM (ad hoc) ) or Taylor series (RoGM (Taylor) ) methods for estimating variances have been proposed as alternative effect measures for skewed continuous data. Skewed data are suggested for summary measures of clinical parameters restricted to positive values which have large coefficients of variation (CV). Our objective was to compare performance characteristics of RoGM (ad hoc) and RoGM (Taylor) to MD, SMD, and RoM. We used empiric data from systematic reviews reporting continuous outcomes and selected from each the meta-analysis with the most and at least 5 trials (Cochrane Database [2008, Issue 1]). We supplemented this with simulations conducted with representative parameters. Pooled results were calculated using each effect measure. Of the reviews, 232/5053 met the inclusion criteria. Empiric data and simulation showed that RoGM (ad hoc) exhibits more extreme treatment effects and greater heterogeneity than all other effect measures. Compared with MD, SMD, and RoM, RoGM (Taylor) exhibits similar treatment effects, more heterogeneity when CV ≤0.7, and less heterogeneity when CV > 0.7. In conclusion, RoGM (Taylor) may be considered for pooling continuous outcomes in meta-analysis when data are skewed, but RoGM (ad hoc) should not be used. However, clinicians' lack of familiarity with geometric means combined with acceptable performance characteristics of RoM in most situations suggests that RoM may be the preferable ratio method for pooling continuous outcomes in meta-analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.332 | 0.511 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".